Operational Availability Assessment of Tidal Stream Turbines Using Environmental Data and Fuzzy Logic Inference

A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.

Data-Driven Robust Scheduling of a Wind–PV–CSP Hybrid System Using Adaptive Uncertainty Sets

Constructing wind and solar energy bases is an effective way to promote the green transformation, and the optimal dispatch of renewable energy bases is essential to their high-quality development. However, wind and solar generation are characterized by intermittency, fluctuations, and unpredictability, which pose new challenges to the economic operation of power systems. This paper proposes an enhanced data-driven robust optimization method to establish an economic dispatch model for a new energy base that accounts for uncertainties in wind turbine and photovoltaic power output. It also develops uncertainty intervals for wind turbine and photovoltaic output using a random forest regression method. Compared with traditional robust optimization methods, the proposed method fully utilizes historical data to establish a more precise and flexible uncertain variable interval model, avoiding the overly conservative issues inherent in traditional robust optimization approaches. Lastly, the proposed method is validated in a case study, demonstrating that the data-driven uncertainty set is more in line with the actual situation.

Optimization and Improvement of Operation and Maintenance Efficiency of Condition Monitoring Technology for Offshore Wind Turbines

This study proposes an integrated condition-monitoring and predictive-maintenance framework for offshore wind turbines operating in harsh marine environments. To address the challenges of signal degradation, environmental interference, and limited fault-warning capability, a multi-source sensing architecture is developed based on risk-driven sensor deployment, edge-side signal fusion, and intelligent health assessment. Vibration, temperature, strain, and operational signals are adaptively processed through variance-weighted fusion and denoising strategies to improve data reliability. A CNN– LSTM hybrid model is employed for fault feature extraction and temporal degradation analysis, while a digital-twin-driven health assessment framework is used to quantify health indices and remaining useful life. Maintenance scheduling is further optimized by integrating equipment health conditions, resource constraints, and operational windows. Field validation in an offshore wind farm demonstrates that the proposed diagnostic model achieves a fault identification accuracy of 96.2%, while the predicted remaining useful life of the main bearing decreases from 180 days to 16 days before failure. The proposed framework establishes a closed-loop process linking signal acquisition, intelligent diagnosis, lifetime prediction, and maintenance decision-making, providing an effective engineering solution for reliable condition monitoring and intelligent operation of offshore energy systems.

Optimal Operation of Gas Turbine Generator and Energy Storage for Islanded Microgrid AI Data Centers Under Workload Dynamics

The rapid growth of artificial intelligence (AI) data centers introduces highly variable and mission-critical load profiles that challenge conventional power supply strategies. This paper proposes an islanded microgrid gas turbine generator (GTG) and long-duration energy storage (LDES) hybrid architecture to provide both short-term load balancing and extended energy support under prolonged outage conditions. A probabilistic multi-phase workload model is developed to capture the temporal characteristics of training, fine-tuning, and inference processes, incorporating both high-frequency fluctuations and multi-day workload variations. Based on reliability requirements, an LDES sizing methodology is formulated to ensure long-duration autonomy for mission-critical operation in a 12 MW power-block AI data center system, with the storage capacity determined based on a 12-h autonomy criterion. The GTG operating point is then evaluated using four storage performance metrics: charge/discharge transition frequency, charging time ratio, state-of-charge (SoC) deviation, and cumulative energy movement. The results indicate that the optimal GTG operating point ranges from approximately 40–73.3% of the initially selected rating, closely tracking the time-varying average load and significantly reducing LDES utilization and storage stress. While GTG fixed-output operation may induce SoC drift under sustained workload variations, applying the identified optimal operating point maintains SoC within the desired range, demonstrating stable LDES operation without dynamic adjustment. The proposed framework provides quantitative design and operational guidelines for GTG–LDES hybrid systems in next-generation AI data centers.

Load Optimization Distribution Among Cascade Hydropower Stations Based on The A* Algorithm

REAL-TIME ASSESSMENT OF A SOLAR WATER PUMPING SYSTEM UNDER NORMAL AND FAULTY OPERATING CONDITIONS

Solar-powered water-pumping systems are indispensable for ensuring consistent, efficient crop irrigation in remote, off-grid regions. In this paper, an advanced control strategy is implemented using the adaptive neuro-fuzzy inference system (ANFIS) to ensure maximum power extraction under all weather conditions. In addition, the induction motor (whether healthy or with one or more broken rotor bars) is coupled to a centrifugal pump in the solar water pumping system (SWPS). The motor's performance is precisely controlled using direct torque control (DTC). Furthermore, a fast Fourier transform (FFT) is used to monitor the induction motor’s state in the SWPS. Finally, the developed SWPS, operating under both healthy and faulty conditions, is experimentally validated via hardware-in-the-loop (HIL) tests using the OPAL-RT OP5600 platform and the Virtex-6 ML605 FPGA. Thanks to the applied methods, the investigated SWPS exhibits highly satisfactory, reliable performance across varying operating conditions.